Yanshi Hu

AWmeta improves transcriptomic meta-analysis by adaptively weighting studies, yielding more accurate differentially expressed genes with fewer samples and reduced false positives.

This study maps the comorbid landscape of Parkinson's disease and Crohn's disease through a multi-omics approach, revealing shared genetic and transcriptional mechanisms along the gut-blood-brain axis.

HPInet predicts host-pathogen protein-protein interactions using a transformer-based model, achieving high accuracy and identifying key residues from sequence data.

This paper introduces T6CNN, a model that improves T6SE prediction by integrating signal sequences, evolutionary data, and pre-trained features, achieving higher accuracy, sensitivity, and specificity than existing tools.

This study benchmarks APA detection tools in single-cell and spatial transcriptomes, finding SCAPE and scAPAtrap perform best across multiple metrics.

This study systematically analyzes single-cell data to show how transposable elements dynamically regulate the endothelial-to-hematopoietic transition through epigenetic changes and inflammatory signaling.